Differential privacy has been a fruitful target for formal verification due to its compositional property.
Fuzz [1]} and DFuzz [2]} use linear dependent type systems to analyse program sensitivity and prove (pure) differential privacy properties. Amorim et al. [3]} extend such systems to work under approximate differential privacy. Barthe et al. [4]}, [5]}, [6]}, [7]}, [8]} developed several customized relational logics based on probabilistic couplings for reasoning about differential privacy. Zhang and Kifer [9]} introduced the Randomness Alignment technique as a simpler but more restricted alternative of probabilistic coupling. Wang et al. [10]} extend the type system in [9]} to allow more complicated Randomness Alignment functions to be used for sophisticated mechanisms. Albarghouthi and
Hsu [12]} synthesize probabilistic couplings and randomness alignment into coupling strategies, creating the first fully automated tool capable of generating coupling proofs for complex mechanisms.
